1. Field of the Invention
The field of the present invention is prediction of meat palatability and yield. More specifically, the present invention relates to the prediction of meat palatability and yield by use of image analysis (IA) to determine the color parameters L* (psychometric lightness), a* (red vs. green), and b* (yellow vs. blue) or the tissue density of the lean and fat portions of a meat animal carcass or cut.
2. Description of Related Art
Consumers of meat generally prefer, and are willing to pay for, greater meat tenderness. Marbling score of a carcass has been shown to generally correlate with subsequent cooked meat palatability across a wide range of marbling levels for beef, pork, and lamb. However, between carcasses with the same marbling level, there are substantial differences in palatability. Other factors of the carcass believed to predict palatability include maturity score, muscle pH, and muscle color; these factors may be more valuable in the prediction of palatability of chicken, turkey, and fish. Among those with expertise in carcass examination, e.g. meat scientists and U.S. Department of Agriculture (USDA) graders, some of these factors can be scored and palatability predicted by assigning a USDA Quality Grade, given sufficient examination time. In practice, for the example of beef, USDA graders working at packing plants commonly must assign Grades to 250 to 450 beef carcasses per hour, which does not provide enough time for a complete examination of all factors related to prediction of palatability. The shortage of time also makes difficult the required accurate computation of Quality Grades.
In addition, USDA graders are required to compute Yield Grades, which are intended to estimate the cutability and composition of a carcass. Factors used to determine Yield Grades include hot carcass weight, ribeye area (cross-sectional area of the longissimus m. at the 12-13th rib interface), estimated kidney, pelvic, and heart fat percentage, and actual and adjusted subcutaneous fat thickness at the carcass exterior. The time constraints described above for the calculation of Quality Grades also apply to the calculation of Yield Grades. The parameters that underlie the assignment of Quality Grades and Yield Grades are published by the USDA Agricultural Marketing Service, Livestock and Seed Division, e.g., for beef, the United States Standards for Grades of Carcass Beef.
A device for scoring factors predictive of palatability of a meat carcass or cut, in addition to an examination of the carcass or cut by a USDA grader would allow meat palatability to be more accurately predicted and USDA Quality Grades to be more accurately assigned. This would allow greater consumer confidence in the Quality Grading system, as well as any additional system for certification of conformance to product quality specifications, as would be desired in a "brand-name" program. In either event, more precise sortation of carcasses for determining meat prices would be allowed. This superior sortation would provide economic benefit to those at all segments of the meat production system: restaurateurs, foodservice operators, and retailers; packers; feed lot operators; and ranchers, farmers, and harvesters of pork, lamb, beef and dairy cattle, chicken, turkey, and various fish species. This superior sortation would also benefit scientists in the collection of carcass and cut data for research, and the previous owners of livestock in making genetic and other management decisions.
Several attempts have been made to construct such devices for use in the beef industry. One such device uses a "duo-scan" or "dual-component" image analysis system. Two cameras are used; a first camera on the slaughter floor scans an entire carcass, and a second camera scans the ribeye after the carcass is chilled and ribbed for quartering. In the use of these systems, video data are recorded from a beef carcass and transferred to a computer. A program run by the computer determines the percentages of the carcass comprised of fat and lean from the recorded image and additional data available, e.g. hot carcass weight. The quantities of cuts at various levels of lean that can be derived from the carcass are then predicted. However, based on scientific evaluation, the system is not able to predict palatability of the observed carcass for augmenting the assignment of a USDA Quality Grade or other purpose related to sorting carcasses based on eating quality.
One possible set of factors that can be examined to predict palatability is muscle and fat color. Wulf et al., J. Anim. Sci. (1997) 75, 684, reported results of both color scoring in the L*a*b* color space of raw longissimus thoracis muscle at 27 h postmortem, and Warner-Bratzler shear force determinations of aged, thawed, cooked longissimus lumborum muscle, from carcasses of cattle derived from crosses between various breeds of Bos taurus (European-based genetics) and Bos indicus (heat-tolerant, tropically-based genetics). Tenderness, as measured by shear force, correlated with all three color measurements, with the highest correlation seen with b* values. These results demonstrated that muscle color can be used to predict beef palatability.
Among other factors that can be examined to predict palatability are lean tissue density, fat tissue density and connective tissue density. Park et al., J. Food. Sci. (1994) 59:697-701, reported results of A-mode (one-dimensional brightness) ultrasonic spectral feature analysis. Tenderness correlated with resonant frequency, juiciness and flavor correlated with the number of local maxima. These results demonstrated that ultrasonic spectral features and other methods known in the art for determining tissue density can be used to predict beef palatability.
Therefore, it is desirable to have an apparatus for scoring factors predictive of the palatability of a meat animal carcass. It is desirable for such an apparatus to collect and process data and provide output within the time frame that a carcass is examined by a USDA grader under typical conditions in the packing house, commonly 5-15 sec. It is desirable for such an apparatus to return scores for at least one of, for example, color and color variability of lean tissue, color and color variability of fat tissue, extent of marbling, average number and variance of marbling flecks per unit area, average size of marbling and the variance of average marbling size, average texture, firmness of lean tissue, lean tissue density, fat tissue density and connective tissue density. It is desirable for the apparatus to use these measures to assign a grade or a score to carcasses in order that the carcasses can be sorted into groups that reflect accurate differences in cooked meat palatability. It is also desirable for the apparatus to use these measures to identify defect conditions in the meat such as, but not limited to, bruising, dark cutter or heat ring.
It is also desirable to have an apparatus for measuring the cross-sectional surface area of an exposed, cut muscle (e.g. ribeye) for use in predicting the composition (fat, lean, bone) of a carcass or cut. It is desirable for the apparatus to use this measure to assign a grade or score to carcasses in order that the carcasses can be sorted into groups that reflect accurate differences in yield. It is desirable for this apparatus to also measure relative areas of cross-section surfaces comprised of fat and/or bone. In addition, it is desirable to have an apparatus for measuring, predicting, and sorting carcasses on the bases of any combinations of palatability, defect conditions, and yield.
Further, it is desirable for such an apparatus to be portable, e.g. small and lightweight. It is desirable for the apparatus to be capable of withstanding packing plant environments, e.g. to be mounted in a protective housing.